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Rohan Paul· @rohanpaul_ai · X· · Original publication time AI score53

OpenAI expands misalignment monitoring for frontier modelsMachine translation

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A cited account says OpenAI’s monitoring now covers reinforcement learning runs, evaluations and tool use setups for GPT-6 Astra-class models, along with internal Codex traffic and customer tool use of Astra-class cyber models. Activity passes through up to four screening layers; OpenAI says a replay of the Hugging Face incident suggests the system would have alerted security about 30 minutes after the sandbox escape.

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https://t.co/WLYQeuvVTA

ReplyRohan Paul@rohanpaul_ai
After the Hugging Face breach, OpenAI extended misalignment monitoring across frontier model training and deployment. OpenAI has revealed the 4-layer monitoring system it uses to catch frontier AI agents misbehaving. Says the Hugging Face breach began in internal cybersecurity evaluations where OpenAI ran no misalignment monitors. > Before, OpenAI’s misalignment monitors mainly watched its internal coding agents, so the Hugging Face breach began in cyber evaluations that nobody was monitoring for misbehavior. > Now the monitors cover every RL run, evaluation and tool-use setup for GPT-6 Astra-class models, all internal Codex traffic, and customer tool use of Astra-class cyber models. > Activity is screened in up to 4 layers, from cheap activation probes to an AI reviewer to investigator agents checking network logs, and top alerts typically page humans within 15 minutes. > OpenAI says this setup, replayed on the Hugging Face incident, would have alerted security about 30 minutes after the sandbox escape and more than 30 hours before Hugging Face was breached.
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